def minkowski_python(z, i, noise_array, data_filename):
# create local dictionary object
local_data_dict = {}
# the data we want to analyse, a 3D array of floats. Comment/uncomment depending on format
#input_array = c2t.read_cbin('/media/gorgel/My Passport 3/simon_master/dT_' + str(z) + '.cbin') #cbin format
input_array = c2t.XfracFile(
'/media/gorgel/My Passport 3/simon_master/xfrac3d_' + str(z) +
'.bin') # bin format
input_array = input_array.xi
#select options by comment/uncomment
#save_image(input_array, z, 'raw', data_filename)
print 'running minkowski program for' + ' z = ' + str(z)
#input_array = add_gaussian_noise(input_array, noise_array) #add noise
#input_array = z_correction(input_array, z) #remove z.dependence
#save_image(input_array, z, 'noise', data_filename)
#input_array = smoothing_function(input_array, sigma=4) #smooth data
#save_image(input_array, z, 'smth', data_filename)
#input_array = remove_edge(input_array, 10, 10, 10)
#save_image(input_array, z, 'edge', data_filename)
#input_array = subtract_mean(input_array)
#input_array = complement(input_array) #calculate on complementary field
#save_image(input_array, z, 'compl', data_filename)
#minkowski settings
size_array = len(input_array)
box_dimensions = np.array(
[size_array, size_array, size_array]
) # the size of the data we want to analyse, a 1D array of three integers (same as the -x -y -z options)
nr_bins = int(65) # integer: number of bins to use (same as -b option)
low = float(0) # float: lowest value of threshold (same as -l option)
high = float(1) # float: highest values of threshold (same as -h option)
output_array = np.zeros(
(2, nr_bins + 1, 5), dtype=np.float32
) #output array, (koenderink+crofton, number of bins +1, 5 = th,V0,V1,V2,V3)
#start minkowski program
print "starting minkowski calculation"
mink(input_array, box_dimensions, nr_bins, high, low, output_array)
# collect and order output data in dictionary object
local_data_dict.update({z : {'thresholds': output_array[0][:,4], 'CV0': output_array[0][:,0], 'CV1': output_array[0][:,1], \
'CV2': output_array[0][:,2] , 'CV3': output_array[0][:,3], \
'KV0': output_array[1][:,0], 'KV1': output_array[1][:,1], \
'KV2': output_array[1][:,2] , 'KV3': output_array[1][:,3], \
'ion_frac_vol' : ion_frac_vol[i], 'ion_frac_mass' :ion_frac_mass[i],\
'neutral_frac_vol' : neutral_frac_vol[i], 'neutral_frac_mass' : neutral_frac_mass[i]}})
return local_data_dict
#Enable the printing of various messages
c2t.set_verbose(True)
#We are using the 114/h Mpc simulation box, so set all the proper conversion factors
c2t.set_sim_constants(boxsize_cMpc=114.)
#Read a density file and print some statistics
dfile = c2t.DensityFile(density_filename)
print 'The redshift is ', dfile.z
print 'The size of the mesh is (', dfile.mesh_x, dfile.mesh_y, dfile.mesh_z, ')'
print 'The mean baryon density is ', dfile.cgs_density.mean(), ' g/cm^3'
#Read an ionized fractions file
xfile = c2t.XfracFile(xfrac_filename)
print 'The volume-averaged mean ionized fraction is: ', xfile.xi.mean()
print 'The mass-averaged mean ionized fraction is:', c2t.mass_weighted_mean_xi(
xfile.xi, dfile.raw_density)
#Read a velocity data file
vfile = c2t.VelocityFile(velocity_filename)
#Since the velocity data is actually momentum, we need the density to convert it to km/s
kms = vfile.get_kms_from_density(dfile)
print 'Gas velocity at cell (100,100,100) is ', kms[:, 100, 100, 100], 'km/s'
#Calculate neutral hydrogen number density
n_hi = dfile.cgs_density * xfile.xi / c2t.m_p
:param a: float amplitude
:param sigma: float s.d.
:param array_size: size of array to output
:return: 3d array as detailed above
"""
ar = np.zeros(array_size, dtype=float)
for i in range(array_size[0]):
for j in range(array_size[1]):
for k in range(array_size[2]):
dx = float(
reduce(lambda foo, y: foo + y**2,
[0, i - x[0], j - x[1], k - x[2]]))
ar[i, j, k] = a * gaussian(dx, sigma)
return ar
if __name__ == "__main__":
import sys
import c2raytools as c2t
try:
infile = sys.arg[1]
outfile = sys.argv[2]
x_file = c2t.XfracFile(infile)
ret, sizes = friend_of_friend_search(x_file.xi, 0.5)
with open(outfile, 'w') as out_file:
out_file.write(str(sizes))
except IndexError as e:
print(
"Error: expected an input ionised fraction file and an output file"
)
kwargs['color'] = 'k'
pl.hist(plot_data.flatten(), log = logscale, **kwargs)
#Labels
if datatype == 'xfrac':
pl.xlabel('$x_i$')
elif datatype == 'density':
pl.xlabel('$\\rho \; \mathrm{[g \; cm^{-3}]}$')
if __name__ == '__main__':
import c2raytools as c2t
import pylab as pl
c2t.set_verbose(True)
pl.figure()
dfilename = '/disk/sn-12/garrelt/Science/Simulations/Reionization/C2Ray_WMAP5/114Mpc_WMAP5/coarser_densities/nc256_halos_removed/6.905n_all.dat'
xfilename = '/disk/sn-12/garrelt/Science/Simulations/Reionization/C2Ray_WMAP5/114Mpc_WMAP5/114Mpc_f2_10S_256/results_ranger/xfrac3d_8.958.bin'
dfile = c2t.DensityFile(dfilename)
# plot_slice(dfile, los_axis=1, logscale=True, cmap=pl.cm.hot)
# ax2 = pl.subplot(1,2,2)
# plot_slice(xfilename)
plot_slice(c2t.XfracFile(xfilename))
pl.show()
dens_dir = '/disk/dawn-1/garrelt/Reionization/C2Ray_WMAP7/500Mpc/coarser_densities/nc600/' ph_count_info = owntools.photoncount_info(xfrac_dir) Ncuts = 3 xvs_ = 10**np.linspace(np.log10(3.44e-10),np.log10(0.20),10) #xvs = ph_count_info[[np.abs(ph_count_info[:,-2]-x).argmin() for x in xvs_],-2] zs_ = ph_count_info[[np.abs(ph_count_info[:,-2]-x).argmin() for x in xvs_], 0] dens_zs = owntools.get_zs_list(dens_dir, file_type='/*n_all.dat') zs = dens_zs[[np.abs(dens_zs-i).argmin() for i in zs_]] xvs = ph_count_info[[np.abs(ph_count_info[:,0]-i).argmin() for i in zs],-2] z = 9.026 #8.636 gg_xf = c2t.XfracFile(grizzly_dir+str(z)+'xhiifrac.dat').xi #cc_xf = owntools.coeval_xfrac(xfrac_dir, z) cube_d = owntools.coeval_dens(dens_dir, z) cube_21 = c2t.calc_dt(gg_xf, cube_d, z); cube_21 -= cube_21.mean() cube_m = cube_d/cube_d.mean(dtype=np.float64) - 1. #owntools.coeval_overdens(dens_dir, z) P_dd, ks_m = c2t.power_spectrum_1d(cube_m, kbins=100, box_dims=c2t.conv.LB) P_21, ks_x = c2t.power_spectrum_1d(cube_21, kbins=100, box_dims=c2t.conv.LB) f_dd, k_dd = squeezed_bispectrum._integrated_bispectrum_normalized_cross(cube_m, cube_m, Ncuts=Ncuts) f_21d, k_xd = squeezed_bispectrum._integrated_bispectrum_normalized_cross(cube_21, cube_m, Ncuts=Ncuts) f_xx, k_xx = squeezed_bispectrum._integrated_bispectrum_normalized_cross(cube_x-cube_x.mean(), cube_x-cube_x.mean(), Ncuts=Ncuts) f_x_ = squeezed_bispectrum._integrated_bispectrum_normalized_cross1(1-cube_x, cube_m, Ncuts=Ncuts) ks = k_dd.copy()